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Human Activity Recognition (HAR) using wearable and mobile sensors has gainedmomentum in last few years, in various fields, such as, healthcare,surveillance, education, entertainment. Nowadays, Edge Computing has emerged toreduce communication latency and network traffic.Edge devices are resourceconstrained devices and cannot support high computation. In literature, variousmodels have been developed for HAR. In recent years, deep learning algorithmshave shown high performance in HAR, but these algorithms require lot ofcomputation making them inefficient to be deployed on edge devices. This paper,proposes a Lightweight Deep Learning Model for HAR requiring less computationalpower, making it suitable to be deployed on edge devices. The performance ofproposed model is tested on the participants six daily activities data. Resultsshow that the proposed model outperforms many of the existing machine learningand deep learning techniques.

A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices